Parallelizing MCMC via Weierstrass sampler X Wang, DB Dunson Joint Statistical Meetings 2014, 2014 | 206* | 2014 |
High dimensional ordinary least squares projection for screening variables X Wang, C Leng Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2016 | 146 | 2016 |
Parallelizing MCMC with random partition trees X Wang, F Guo, KA Heller, DB Dunson Advances in neural information processing systems 28, 2015 | 86 | 2015 |
Boosting variational inference F Guo, X Wang, K Fan, T Broderick, DB Dunson Advances in Approximate Bayesian Inference, NIPS 2016 Workshop, 2016 | 81 | 2016 |
A direct approach for sparse quadratic discriminant analysis B Jiang, X Wang, C Leng Journal of Machine Learning Research 19 (31), 1-37, 2018 | 64 | 2018 |
Median selection subset aggregation for parallel inference X Wang, P Peng, DB Dunson Advances in neural information processing systems 27, 2014 | 31 | 2014 |
DECOrrelated feature space partitioning for distributed sparse regression X Wang, DB Dunson, C Leng Advances in neural information processing systems 29, 2016 | 28 | 2016 |
Towards unifying Hamiltonian Monte Carlo and slice sampling Y Zhang, X Wang, C Chen, R Henao, K Fan, L Carin Advances in Neural Information Processing Systems 29, 2016 | 23 | 2016 |
No penalty no tears: Least squares in high-dimensional linear models X Wang, D Dunson, C Leng International Conference on Machine Learning, 1814-1822, 2016 | 22 | 2016 |
On the consistency theory of high dimensional variable screening X Wang, C Leng, DB Dunson Advances in Neural Information Processing Systems 28, 2015 | 10 | 2015 |
Boosting variational inference: theory and examples X Wang Master's thesis, Duke University, 2016 | 9 | 2016 |
Distributed Feature Selection in Large n and Large p Regression Problems X Wang Duke University, 2016 | | 2016 |